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arXiv · 2511.05951

Klear-AgentForge: Forging Agentic Intelligence through Posttraining Scaling

Qi Wang·Hongzhi Zhang·Jia Fu·Kai Fu·Yahui Liu·Tinghai Zhang·Chenxi Sun·Gangwei Jiang·Jingyi Tang·Xingguang Ji·Yang Yue·Jingyuan Zhang·Fuzheng Zhang·Kun Gai·Guorui Zhou
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Abstract

Despite the proliferation of powerful agentic models, the lack of critical post-training details hinders the development of strong counterparts in the open-source community. In this study, we present a comprehensive and fully open-source pipeline for training a high-performance agentic model for interacting with external tools and environments, named Klear-Qwen3-AgentForge, starting from the Qwen3-8B base model. We design effective supervised fine-tuning (SFT) with synthetic data followed by multi-turn reinforcement learning (RL) to unlock the potential for multiple diverse agentic tasks. We perform exclusive experiments on various agentic benchmarks in both tool use and coding domains. Klear-Qwen3-AgentForge-8B achieves state-of-the-art performance among LLMs of similar size and remains competitive with significantly larger models.

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Qi Wang, Hongzhi Zhang, Jia Fu, Kai Fu, Yahui Liu, Tinghai Zhang, Chenxi Sun, Gangwei Jiang, Jingyi Tang, Xingguang Ji, Yang Yue, Jingyuan Zhang, Fuzheng Zhang, Kun Gai, Guorui Zhou. 2025-11-08. Klear-AgentForge: Forging Agentic Intelligence through Posttraining Scaling. https://arxiv.org/abs/2511.05951

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